BackgroundTemporal muscle thickness (TMT) has emerged as an imaging marker of skeletal muscle status and may be associated with cognitive decline in Alzheimer’s disease (AD). However, its relationship with AD severity remains unclear, and the clinical relevance of masseter muscle thickness (MMT) is unknown.ObjectiveTo evaluate the associations of TMT and MMT with cognitive function and disease severity in AD.MethodsThis retrospective study included 242 patients with AD between December 2023 and April 2025. TMT and MMT were measured on axial T1-weighted MRI, cognitive function was assessed using standardized neuropsychological tests, and disease severity was determined by Clinical Dementia Rating (CDR).ResultsThe intraclass correlation coefficients (ICC) for the mean TMT and MMT were 0.868 and 0.896, respectively, indicating good consistency. As the severity of AD progresses, TMT tends to be thinner in severe AD than in mild AD. Spearman’s correlation analysis revealed that both TMT and MMT were correlated with the Mini-Mental State Examination (MMSE) and CDR. After adjusting for various confounding factors using ordered logistic regression and multiple linear regression, TMT was found to be independently associated with AD severity (OR: 0.424, 95% CI: 0.290–0.618, p < 0.001) and positively correlated with MMSE scores. MMT showed only a weak independent association with CDR staging (OR: 0.830, 95% CI: 0.724–0.952, p = 0.008), while no significant correlation was observed with the MMSE. Stratified analysis revealed that the TMT maintained a consistent association in both male and female patients, whereas the MMT achieved statistical significance only in females in ordered logistic regression; all other findings were not statistically significant.ConclusionTemporal muscle thickness is associated with cognitive impairment and clinical severity in patients with AD and may serve as a simple, noninvasive imaging marker; in contrast, the independent associations of MMT with disease severity were weak and sex-specific, suggesting that its clinical utility may be limited and requires further validation.
Effect of age-friendly community degree on the cognitive function of older adults
IntroductionThe relationship between individual environmental indicators and the physical and mental health of older adults has been widely studied. However, the holistic impact of age-friendly communities on the wellbeing of older adults has not beenclearly elucidated. This study aimed to explore the correlation between the degree of community friendliness and cognitive function of older adults and provide scientific evidence for healthy aging.MethodsThis cross-sectional study enrolled 450 residents aged ≥60 years with cognitive impairment in Ningbo, Zhejiang Province, from June to September 2024. Participants provided personal demographic data, and their community’s age-friendly status and cognitive function were assessed using the Age-Friendly Community (AFC) Evaluation Scale and the Alzheimer’s Disease 8 Questionnaire. A multivariate logistic regression analysis was performed to examine the relationship between demographic characteristics and cognitive function of the older adults.ResultsAmong the five dimensions of the AFC evaluation scale, community participation scored the highest at 3.56 ±’ 1.21 points. Multivariate linear regression analysis revealed that personal demographic factors (age, sex, education level, economic status, housing conditions, alcohol consumption, and chronic diseases) significantly infiuenced cognitive function (all P < 0.05). Furthermore, residential environment, transportation, and community environment were identified as significant predictors of cognitive impairment (all P < 0.05).DiscussionLow scores in residential environment, transportation, community environment, and social inclusion in AFC are independent risk factors for cognitive dysfunction. Measures to enhance community age-friendly environments could help improve the cognitive function of older adults.
Pilot clinical study of Taxifolin Aqua effect in patients with amnestic mild cognitive impairment
Taxifolin (dihydroquercetin), a potent antioxidant flavonoid, shows promise for neurodegenerative conditions. Our pilot study aimed at evaluation the efficacy and safety of Taxifolin in patients with amnestic mild cognitive impairment (aMCI) and monitored a set of blood measures. Twenty-three patients (18 female; aged 53-87) received dihydroquercetin oral solution for 8 weeks with a 4-week follow-up period in an open-label study design. Thirteen patients with aMCI were monitored as controls. Outcomes were assessed using cognitive status (MMSE, MoCA, FCSRT-IR), neuropsychiatric symptoms’ (NPI-Q, HDRS), and rater’s impression (CGI) scales, along with blood measures monitoring (glutathione reductase (GR), glutamate dehydrogenase (GDH), and superoxide dismutase (SOD); plasma neutrophil elastase (NE), alpha-1-proteinase inhibitor (alpha-1PI)). Responder classification using composite Z-score changes was based on all clinical scales. Results: Eleven patients were classified as responders, showing significant MMSE and MoCA improvement (mean +2.73 points, p=0.001) sustained through 4-week follow-up. Non-responders and control group showed slight cognitive decline. Composite Z-score changes at the 4-week follow-up revealed differences between responders/non-responders (ANOVA F(2,20)=17.38, p=0.001); biomarker analysis revealed difference in NE/alpha-1PI ratio (p=0.034), SOD (p=0.023), and GR dynamics (p=0.022), which correlated negatively with clinical response (R=-0.547, p=0.007), and baseline GDH (p=0.049). Conclusion: Taxifolin treatment resulted in significant cognitive benefits in ~half of aMCI patients, with effects sustained for one month post-treatment. Biomarker changes reveal possible mechanisms involving oxidative stress modulation and inflammatory pathways. These findings support further controlled trials of Taxifolin in populations at risk of Alzheimer’s disease.
Perinatal Mental Health Detection and Prediction Using Mobile Sensing Data: Systematic Review
Background: Perinatal mental health disorders affect approximately 20% of pregnant and postpartum individuals, and are associated with substantial maternal and infant morbidity. Traditional assessment relies on infrequent, subjective self-reports. Mobile devices, including smartphones and wearables, offer opportunities for continuous and objective measurement, but evidence on their assessment utility in perinatal populations remains fragmented. Objective: This review aimed to examine the application of wearable devices and smartphones for detecting and predicting perinatal mental health outcomes, with emphasis on predictive performance, informative features, and methodological rigor. Methods: We conducted a systematic review following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines (PROSPERO CRD420251249218). Six databases (PubMed, Web of Science, Scopus, PsycINFO, IEEE Xplore, and ACM Digital Library) were searched initially in January 2026 and supplemented by an amended search in June 2026, with no publication date restrictions. Evidence was synthesized narratively, and the risk of bias was assessed using PROBAST+AI (Prediction Model Risk of Bias Assessment Tool With AI extension). Results: The initial and supplementary searches yielded 1952 unique records after deduplication, of which 10 studies met the inclusion criteria. The included studies covered postpartum depression, prenatal stress, discrete emotions during pregnancy (eg, happiness, anxiety, and sadness), and maternal loneliness. High discrimination metrics were reported for postpartum depression in individual studies, including a multiclass area under the curve of 0.85, a binary area under the curve of 0.871, and an -score of 0.9872. Heart rate variability, GPS-derived mobility, physical activity, and sleep features were most frequently reported as useful, and their interpretation requires perinatal-specific contextualization. Methodological quality was limited, with 80% (12/15) of PROBAST+AI assessment units rated as having high overall quality concern or risk of bias, mainly due to small samples, limited validation, inadequate handling of missing data, and potential overfitting in the analysis domain. Conclusions: Mobile sensing shows preliminary potential for perinatal mental health assessment, but current evidence does not yet support clinical screening or decision-making, and independent external validation in perinatal populations is currently lacking. Progress toward clinical utility requires broader mental health outcome coverage, larger longitudinal cohorts, standardized analytical and reporting practices, adoption of modeling approaches better suited to perinatal trajectories, independent external validation, and human-centered monitoring designs.
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Possible Role and Function of AI Conversational Agents in Dialectical Behavior Therapy for Borderline Personality Disorder: Qualitative Interview Study
Background: Borderline personality disorder (BPD) is associated with substantial distress and a high risk for suicide. Individuals with BPD may be unable to access evidence-based treatments like dialectical behavior therapy (DBT). Artificial intelligence conversational agents (AI-CA) are increasingly discussed as scalable tools for mental health support, but little is known about how DBT clinicians understand the possible role of AI-CA in treatment. Objective: This study aimed to explore DBT psychologists’ perspectives on integrating AI-CA into DBT for BPD in the future. Methods: Seventeen psychologists in Sweden, each with at least 1 year of clinical DBT experience (mean 6.4 years, SD 5.6), participated in semistructured interviews as part of this qualitative study. Interviews were conducted in Swedish, transcribed verbatim, and analyzed using reflexive thematic analysis within a constructivist framework. Participants did not test a specific AI-CA. Results: Three main themes were developed from the data. The first main theme, “Who Are We in Therapy?” explored how participants defined AI-CA relationally, positioning it variously as a tool, team member, or supervisor, and a sometimes harmful competitor. How these positionings were configured shaped what AI-CA was seen as allowed to do. The second main theme, “The Stoic Helper,” captured how AI-CA was constructed as an extension of the ideal helper: available, competent, adaptable, and tireless, able to provide support in moments when human therapists could not or preferred not to be present. Participants’ hopes for what AI-CA could become often mirrored qualities they found difficult to sustain in their own clinical work. The third main theme, “The Well-Intended Accommodator,” captured concerns that AI-CA may reinforce dependency and function as a safety behavior by supporting reassurance-seeking rather than autonomy. A central concern was not whether AI-CA could generate validating responses, but whether it could know when validation supports change and when it becomes maladaptive accommodation (AI functional ambiguity). Conclusions: Perceived benefits mainly centered on accessibility and support for DBT skills generalization, whereas key concerns involved alliance disruption, reinforcement of behaviors that would ideally be targeted for change, dependency, and questions regarding responsibility in high-risk situations. Integrating AI-CA into DBT is not only a technical question but a relational and ethical one. How AI-CA is positioned in relation to the therapist, person in treatment, and team shapes which tasks are considered acceptable and what form integration can take. The findings highlight the need for implementation frameworks that account for relational dynamics, treatment-specific considerations, and AI functional ambiguity that may arise when AI-CA operates in complex therapeutic contexts.
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Assessing the Need for Mental Health Support From Free-Text Responses: Development and Validation of Language-Based Assessments in Adults With Internalizing Symptoms
Background: Machine learning and natural language processing have demonstrated significant potential for mental health assessment: describing your mental health in your own words can offer a more ecologically valid approach than traditional rating scales. However, most models focus on specific diagnoses, conditions, or symptoms, which may prematurely assign labels and potentially reinforce stigma in the context of early-stage mental health screening. Objective: This study develops a language-based assessment model that assesses the need for mental health support based on probed natural language and validates it against best-estimate assessments from multiple experienced psychotherapists. Methods: We analyzed an enriched online sample (n=600 for development and n=212 for validation), in which about half reported experiencing internalizing symptoms (depression or anxiety). Participants described their mental health using open-ended responses regarding (1) mental health, (2) suicidal thoughts, (3) medical history, and (4) depression. The responses were converted into contextual word embeddings using a large language model and entered as predictors in a ridge regression using nested cross-validation. Two to three experienced psychotherapists assessed each participant’s need for mental health support on a scale from 1 (no support needed) to 5 (potential crisis). Their assessments were based on longitudinal clinical data (natural language, validated scales, clinical interview, sociodemographics, and clinical history) and were averaged into a best-estimate assessment for model validation. We used the Sequential Evaluation With Model Preregistration framework, which separates model development from validation in a held-out set to support robust estimations and generalizability. Results: The language-based assessments closely aligned with the best-estimate assessments (=0.82) and showed strong correlations with established clinical rating scales for depression (Patient Health Questionnaire-9), anxiety (Generalized Anxiety Disorder 7-Item Scale), stress (Perceived Stress Scale 10), and suicidality (Inventory of Depression and Anxiety Symptoms; =0.62-0.77). Language-based visualizations of topics and word embeddings showed that low need for support assessments was associated with mentioning well-being and good health, while high assessments were related to depression, anxiety, and suicidality. Conclusions: This study demonstrates that natural language responses analyzed through large language models and machine learning can be used to assess individuals’ need for mental health support in close alignment with best-estimate assessments from experienced psychotherapists. Using less than 5 minutes of respondent time, this approach offers a practical tool for early-stage mental health screening in both clinical and self-guided screening contexts.
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Metabolic dysfunction associates with neuroimaging measures of brain structure and connectivity
IntroductionMetabolic syndrome (MetS) and insulin resistance are established risk factors for Alzheimer’s disease (AD) and have been linked to alterations in brain structure and function, including within the Default Mode Network (DMN).MethodsHere, we examined associations between metabolic dysfunction, brain structure, and DMN functional connectivity using data from the Health and Aging Brain Study: Health Disparities (HABS-HD) cohort. Participants were classified as being cognitively unimpaired (CU; n = 1,045; mean age = 66.1 years; 65% female), or having mild cognitive impairment (MCI; n = 186; mean age = 66.5 years; 52% female) or dementia (n = 76; mean age = 69.0 years; 47% female).ResultsAcross diagnostic groups, cortical thickness in AD-vulnerable regions varied by both cognitive status and HbA1c grouping, with the strongest effects observed in the CU cohort. HbA1c grouping was also associated with white matter microstructure, including stepwise reduc tions in fractional anisotropy of the anterior corona radiata across glycemic cat egories (normoglycemia > prediabetes > diabetes) in CU individuals. Functional connectivity of the posterior DMN (pDMN) showed main effects of both cognitive status and HbA1c grouping. Using a continuous index of cardiometabolic risk (MetS-Z), pDMN connectivity decreased linearly with increasing cardiometabolic burden in the CU cohort, whereas other DMN subnetworks exhibited positive associations with MetS-Z.DiscussionThese findings suggest network-specific associations between cardiometabolic risk and DMN organization that are detectable in indi viduals with MCI, a clinical state preceding dementia, as well as in some degree within cognitively unimpaired individuals. Longitudinal analyses are needed to determine whether metabolic dysfunction predicts divergent trajectories of DMN connectivity over time.
Editorial: Panoramic view of cognitive impairment: interdisciplinary cognitive research, volume II
Trauma-informed psychological support alongside long-acting injectable buprenorphine: clients’ perspectives on the Buvidal psychological support service (BPSS) pilot in Wales
BackgroundOpioid use disorder (OUD) is associated with persistent drug use, high relapse rates, and significant morbidity, mortality, and psychosocial harm. While Long-Acting Injectable Buprenorphine (LAIB), marketed as Buvidal, offers improved pharmacological management over traditional opioid substitution therapy (OST), the mental clarity provided by LAIB often surfaces unresolved trauma and mental health needs. Integrating trauma-informed psychological care alongside pharmacological intervention is increasingly recognized as critical to optimize recovery outcomes for individuals with co-occurring psychiatric co-morbidities and substance use disorder.ObjectiveThis evaluation aimed to explore client experience of the Buvidal Psychological Support Service, a tiered trauma-informed psychological service, piloted to support individuals with OUD receiving LAIB in Wales, UK.MethodsQualitative data were derived from nine semi-structured interviews with BPSS service users, exploring their experiences, perceived impacts, and recovery journeys through reflexive thematic analysis.ResultsFindings indicated the transition to LAIB was often transformative but exposed trauma and emotional difficulties previously masked by substance use, making the availability of timely psychological support critical for maintaining abstinence and wellbeing. Clients valued the BPSS’s rapid access, continuity, person-centered care and non-judgmental ethos, reporting enhanced agency, emotional regulation, and social reintegration. Relapse prevention was strongly linked to psychological support alongside pharmacological treatment.ConclusionThe findings suggest that participants experienced BPSS as a valuable adjunct to LAIB, particularly where increased mental clarity brought trauma-related and emotional difficulties into focus. The pilot highlights the potential value of integrating trauma-informed psychological support within LAIB pathways, while further evaluation is needed to assess transferability and longer-term outcomes.
Factors associated with school absenteeism in adolescents with mental illness: a multivariate analysis
Backgroundschool absenteeism is a common functional impairment among adolescents with mental disorders, yet its associated factors remain limited. This study aimed to identify key demographic, clinical, and social factors associated with school absenteeism in this population.MethodsA retrospective cohort study was conducted among 217 adolescents (12–18 years) with mental illness, categorized into school-attending (n=163) and school-suspended (n=54) groups. Baseline data on demographics, clinical characteristics, social support, and family factors were collected. Univariate analyses were used to compare group differences, followed by multivariate logistic regression and restricted cubic spline (RCS) analysis to identify independent predictors of school absenteeism.ResultsIn univariate analysis, school-suspended adolescents had significantly lower social support scores (36.2 ± 10.9 vs. 45.5 ± 12.2, p<0.001), higher rates of parental divorce (46.3% vs. 22.1%, p<0.001), and a higher proportion of pre-hospitalization school absenteeism (51.9% vs. 31.3%, p=0.006). Multivariate logistic regression revealed that parental divorce (OR = 3.04, 95%CI=1.59–5.83, p=0.001), pre-hospitalization school absenteeism (OR = 2.37, 95%CI=1.26–4.43, p=0.007), and longer disease course (OR = 1.02, 95%CI=1.00–1.04, p=0.022) were independent risk factors for school absenteeism. Conversely, higher social support was a strong protective factor: compared to the low social support group (score 17–34), the medium (35–51) and high (52–85) support groups had markedly lower odds of school absenteeism (OR = 0.24, 95%CI=0.08–0.74, p=0.013; OR = 0.05, 95%CI=0.01–0.24, p<0.001, respectively). RCS analysis confirmed a linear negative association (P for non-linearity=0.189), with higher social support scores correlating with progressively lower suspension risk.ConclusionsParental divorce, pre-hospitalization school absenteeism, and longer disease course are independent risk factors for school absenteeism in adolescents with mental illness, while higher social support acts as a robust protective factor. These findings highlight the need for targeted interventions addressing family stability, social support enhancement, and early intervention for pre-existing school impairment to reduce school absenteeism and promote functional recovery in this vulnerable population.Clinical trial registrationhttps://clinicaltrials.gov/, identifier ChiCTR2100046396.